[1]} in the context of \(\sigma \) -overfitting.
As a Gaussian data augmentation baseline, [1]} use a scheme that is actually different from both 50% and 100% Gaussian augmentation schemes.
They perform Gaussian augmentation on each sample, however they sample the standard deviation uniformly at random from the range \([0, \sigma ]\)We confirmed this implementation detail via private communication with the authors. Note that this also corresponds to what [3]} report in Appendix H.. This strategy can be seen as an interpolation between the 50% and 100% Gaussian augmentation schemes. Moreover, another difference in the results from [1]} compared to our paper is that they computed corruptions for ImageNet-C in-memory which leads to some discrepancy compared to static images (e.g., see Fig. 5 in [1]} for an illustration).
